Gut Bacterial Dysbiosis in Irritable Bowel Syndrome: a Case-Control Study and a Cross-Cohort Analysis Using Publicly Available Data Sets

ABSTRACT Research on the gut microbiota in irritable bowel syndrome (IBS) shows discordant results due to inconsistent study designs or small sample sizes. This study aimed to characterize how gut microbiota in IBS patients differs from that in healthy controls by performing a case-control study and cross- and mega-cohort analysis. Multiple publicly shared data sets were examined by using a unified analytical approach. We performed 16S rRNA gene (V3-4) sequencing and taxonomic profiling of the gut bacterial communities. Fecal samples from children with IBS (n = 19) and age-matched healthy controls (n = 24) were used. Next, we analyzed 10 separate data sets using a unified data-processing and analytical approach. In total, 567 IBS patients and 487 healthy controls were examined. In our data sets, no significant differences existed in stool α-diversity between IBS patients and healthy controls. After combining all the data sets using a unified data-processing method, we found significantly lower α-diversity in IBS patients than in healthy controls. In addition, the relative abundance of 21 bacterial species differed between the IBS patients and healthy participants. Although the causal relationship is uncertain, gut bacterial dysbiosis is associated with IBS. Further functional studies are needed to assess whether the change in gut microorganisms contributes to the development of IBS. IMPORTANCE Research on the gut bacteria in irritable bowel syndrome (IBS) shows discordant results due to inconsistent study designs or small sample sizes. To overcome these issues, we analyzed microbiota of 567 IBS patients and 487 healthy people from 10 shared data sets using a unified method. We demonstrated that gut bacteria are less diverse in IBS patients than in healthy people. In addition, the abundance of 21 bacterial species is different between the two groups. Altered bacterial balance, called dysbiosis, has been reported in several disease states. Although the causal relationship is uncertain, gut bacterial dysbiosis also seems to be associated with IBS.

was largely absent (Figure 1), it was significant with beta diversity when measured by OTUs ( Figure 2). Notably, this significance only remained for the IBS without diarrhea but disappeared for the IBS with diarrhea (IBS-D) subtype. This suggested the IBS patients here maintained an equally rich but distinct microbiome (only IBS without diarrhea) compared to the health controls (HC). The same trend was also observed when measured by ASVs (Figure 3), offering robustness. This distinct microbiome was further illustrated by the differential relative abundance of certain bacterial taxa at the family, genera, species, and ASVs levels ( Figure 4), but not the OTU levels. Because partial 16S does not provide species level resolution, the authors should replace the species plot with an OTU plot in Figure 4. Moreover, the authors should have a 4-box plot instead of the 2-box, i.e., IBS D+ vs IBS D-vs IBS vs HC. Next, the same computational pipeline was applied to a cross-cohort analysis. Again, an association between IBS and alpha diversity was large absent except in one of the 10 cohorts ( Figure 5), while it was significant for beta diversity in majority (n = 6) of the cohorts ( Figure 6). The authors lost this reviewer at Figures 7 and 8, please reconsider if and how to present these two figures. Unlike the case study, the differentially abundant bacteria were not named here, a missed opportunity from both a microbiology (robust microbial markers) and a computational (this analysis vs previous analysis on the same cohort) angle. Lastly, the authors combined all cohorts together for a mega-cohort analysis. This is a great way to increase robustness of their analysis. PCOA plots and differential analysis suggested that the combined dataset had high heterogeneity (Figures 9 and 10), suggesting the necessity for further stratification. While the authors stratified the data via age and cohort exclusion, a more important factor was not considered. That is the subtypes of IBS, including IBS-C (constipation), IBS-D (diarrhea), IBS-M (mixed), which were stratified in most if not all the previous cohorts the authors included here. This is unfortunate in two-folds. One, the authors' own case study already suggested a significant correlation between IBS-D and beta diversity. Two, the megacohort is such a great opportunity to bump up the n for each IBS subtypes which are otherwise small in individual cohorts. If publicly available information does not offer a good match between the 16S data and IBS subtypes, the authors should be proactive and contact PIs of the other cohort studies for that information. The PIs are obligated to share that information for their published studies.
In conclusion, the case, cross-cohort, and mega-cohort studies presented here are of high interest to functional GI researchers. However, the cross-cohort and mega-cohort analyses have significant drawbacks in its current form. This reviewer recommends that authors revise their work accordingly to make it a long-lasting piece. To increase transparency and reproducibility, the authors should also deposit their own and reconditioned datasets publicly per ASM's policy, including the case, cross-cohort, and mega-cohort studies. Specific comments 41, gut bacteria 83, 16S rRNA 84-85, what's the purpose of this step? Please also be more specific -grams of feces per 10ml PBS? filer size? negative control? Please note that 24h of sample processing time is expected to alter the community composition because certain gut bacteria grow very fast. 90, Table 1 is very nice, please link it here and expand it further to list both DNA extraction and sequencing methods for each cohort. 116-118, these are good considerations. 133, reference and rationale using either method? 127, how were archaeal reads handled in both the case and cross-cohort studies? Besides bacteria, 16S sequencing can pick up archaea as well. Of interest, methanogenic archaea have been associated with IBS-C (doi.org/10.1007/s10620-021-06839-0). The Pozuelo cohort even had a specific focus on methanogenic archaea. It will be very interesting if the authors can pull all the archaeal reads from the previous cohorts for a mega-cohort analysis. 150, what IBS symptoms did the 9 without diarrhea have -constipation, bloating, H2 or CH4 positive, etc? 159, a high diversity in IBS with diarrhea is unexpected, please discuss further in the context of other studies reported in the literature. Also, a more informative comparison would be HC vs IBS w/ and w/o diarrhea. 166, The ASVs reproduced the same trend as the OTU analysis -that should be the main message here. Please try not to be overly obsessed with the 0.05 p cutoff, the 0.056 p for the D-vs HC is significant enough when considering both ASVs and OTUs. 169-170, partial 16S can't reliably get to the species level -it'd be better to present both OTUs and ASVs instead in figure 4. Figure 1, briefly explain what each diversity index measures and how it's calculated. All abbreviations should be annotated. Is the P value here FDR adjusted? Figure 4, Replace the species panel with a OTU panel. Figure 6, Indicate what the underlined P values mean -why the Saulnier study was not underlined in figure A? Figure 8, This reviewer has a hard time following the plots here -why not present it the same way as in Figure 5?
Reviewer #2 (Comments for the Author):

Introduction
Line 41: We found that gut bacterias (typo) Line 58: If IBS is likely to develop/or result in less diversity of gut microbes, how does antibiotic administration improve IBS?
The introduction is very light with minimal information---I only got one point which is IBS etiology is unknown and related somehow to microbiota structure.
The introduction needs to be more comprehensive, highlights the advances in the field, emphasize the gap, shows novelty of the study design or approach, or expected breakthrough.

Methods
Line 68: diagnosed with IBS (would the author describes how the diagnosis is made just) Line 72: how the patients got the diagnosis of IBS and there no history of GIT disorder (some patients are as young as 1 year old) Line 74: two weeks without antibiotics is not enough to restore normal gut flora (6 months at least) Line 75: why being obese specifically is an exclusion criterion? Line 77: what do the authors mean by "abnormal endoscopic findings" and why this is an exclusion criterion? Line 83: fecal samples were frozen at −20 {degree sign}C? for how long-It is advised to ultra-freeze at -80{degree sign}C but I would assume that has minimal effect when you extract DNA only and not re-culturing the microbes Line 84: it is not clear how the authors collected the DNA fragments or how they got rid of cell debris and fecal material before DNA extraction----for example, I would use a gradient solution and ultra-centrifuge. Line 85: vibrated for 24 hours? Line 86: PowerSoil DNA Isolation Kit is not appropriate for fecal samples-Could the authors justify they choices?

Results
The conclusion is not supported by the results-In what way does this study introduce a rationale for new therapeutic trials?

Suggestion:
A validation study is required For example: 1) Test the proinflammatory effect of Corynebacteriaceae and Clostridium clostridioforme on GIT cell line such as Caco-2 2) Extract the microbiome cocktail from some stool samples (patients and control) and test the pro or anti-inflammatory effect on cell line---This will help to identify if the gut microbes play a role (as initiation or worsen of the IBS symptoms) Staff Comments:

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Response to reviewers
Reviewer #1 (Comments for the Author): IBS is a significant functional GI disorder affecting many millions of people across the globe.
What causes IBS remains elusive, but it is believed that gut microbiome may play a significant role. Here, the authors first conducted a small case-control study with a focus on associating IBS with alpha and beta bacterial diversity. It appeared that while the association with alpha diversity was largely absent (Figure 1), it was significant with beta diversity when measured by OTUs ( Figure 2). Notably, this significance only remained for the IBS without diarrhea but disappeared for the IBS with diarrhea (IBS-D) subtype. This suggested the IBS patients here maintained an equally rich but distinct microbiome (only IBS without diarrhea) compared to the health controls (HC). The same trend was also observed when measured by ASVs (Figure 3), offering robustness. This distinct microbiome was further illustrated by the differential relative abundance of certain bacterial taxa at the family, genera, species, and ASVs levels ( Figure 4), but not the OTU levels. Because partial 16S does not provide species level resolution, the authors should replace the species plot with an OTU plot in Figure 4.
Moreover, the authors should have a 4-box plot instead of the 2-box, i.e., IBS D+ vs IBS Dvs IBS vs HC.
 Thank you for your valuable comments. We modified Figure 4 according to the reviewer's suggestion. We (1) replaced the phrase 'analysis of the species-level aggregated abundance profiles' with 'analysis of OTU abundances', and (2) further stratified the comparison into HC vs. IBS vs. IBS-D vs. IBS-ND. For a given taxon, the figure was accurate in displaying significant difference between HC vs. IBS-ND; however, significant difference was not observed in the case of HC vs. IBS-D. We are grateful to the reviewer's suggestion, as we are now able to better depict the underlying distinctive features of IBS patients with and without diarrhea. Accordingly, the main text was also modified to emphasize how the observed microbial features of IBS-D and IBS-ND overlapped.
Next, the same computational pipeline was applied to a cross-cohort analysis. Again, an association between IBS and alpha diversity was large absent except in one of the 10 cohorts ( Figure 5), while it was significant for beta diversity in majority (n = 6) of the cohorts ( Figure   6). The authors lost this reviewer at Figures 7 and 8, please reconsider if and how to present these two figures. Unlike the case study, the differentially abundant bacteria were not named here, a missed opportunity from both a microbiology (robust microbial markers) and a computational (this analysis vs previous analysis on the same cohort) angle.
 Figure 7 shows the differential abundance statistics in two dimensions, false discovery rate (i.e., significance) on the Y-axis as well as the magnitude and direction of enrichment (depletion) on the X-axis. Each of the profiled taxa is scattered in the graph, and therefore, we can identify the taxa that are skewed to the left (i.e., less abundant in IBS in this case) or to the right side (i.e., more abundant in IBS). We can also observe taxa with a high significance, i.e., those below the horizontal line demarcating the FDR 0.1 threshold.
Hence, the spots (representing taxa) found in the lower-left or lower-right area of the plots represent the robustly identified differential taxa. We acknowledge that such an explanation was not given in the original manuscript, and its inclusion has now strengthened the figure legend for figure 7. We also are in agreement with the reviewer's opinion that the names of differentially abundant taxa would be informative if given here.
Hence, we inserted a text box within the figure area to name the taxonomy of the differentially abundant taxa. This label excludes the lengthy list of taxa determined from the Zhu 2019 dataset. We also added a sentence in the main text to mention these taxon names.
 Figure 8 shows the alpha-diversity indices of IBS and healthy controls combined across multiple datasets. In this case, we used a histogram instead of a boxplot (or potentially a violin plot) to more clearly reveal if heterogeneity within samples is a result of merging different datasets. In other words, we wanted to assess if there are multiple peaks within the healthy (or IBS) group that originated from the original study rather than being viewed as a result of an after-effect of combining data sets. Hence we are retaining the original format of the figure. However, to better clarify the nature of the plots, we have revised the figure legend for figure 8. We also suspect that the reviewer might have had trouble understanding what comparison each panel (A-D) represents, and thus, we modified the legend to strengthen and clarify our point.
Lastly, the authors combined all cohorts together for a mega-cohort analysis. This is a great way to increase robustness of their analysis. PCOA plots and differential analysis suggested that the combined dataset had high heterogeneity (Figures 9 and 10), suggesting the necessity for further stratification. While the authors stratified the data via age and cohort exclusion, a more important factor was not considered. That is the subtypes of IBS, including IBS-C (constipation), IBS-D (diarrhea), IBS-M (mixed), which were stratified in most if not all the previous cohorts the authors included here. This is unfortunate in two-folds. One, the authors' own case study already suggested a significant correlation between IBS-D and beta diversity.
Two, the mega-cohort is such a great opportunity to bump up the n for each IBS subtypes which are otherwise small in individual cohorts. If publicly available information does not offer a good match between the 16S data and IBS subtypes, the authors should be proactive and contact PIs of the other cohort studies for that information. The PIs are obligated to share that information for their published studies. In conclusion, the case, cross-cohort, and mega-cohort studies presented here are of high interest to functional GI researchers. However, the cross-cohort and mega-cohort analyses have significant drawbacks in its current form. This reviewer recommends that authors revise their work accordingly to make it a long-lasting piece. To increase transparency and reproducibility, the authors should also deposit their own and reconditioned datasets publicly per ASM's policy, including the case, cross-cohort, and mega-cohort studies.
 We added information in the 'availability of data and materials' section as follows: We have uploaded data files to the zenodo with the DOI 10.5281/zenodo.7272051 (https://doi.org/10.5281/zenodo.7272051). Files uploaded include the following: (1) ASV sequences of our samples, (2) ASV read count matrix of our samples, (3)   DNA extraction methods were added to the sequencing method in Table 1. 116-118, these are good considerations.  Thank you. We appreciate the comment.
133, reference and rationale using either method?
 To be more precise, we used the Bray-Curtis method for the intial fecal sample comparisons, as there was less concern about variation in sequencing depth. Later, we performed cross-cohort comparisons using published datasets. We found that CLR-based Aitchison distance is a more robust method compared to simple rarefaction with respect to depth when dealing with variable sequencing depth. Hence, in the cross-cohort comparisons, we performed a beta-diversity analysis based on the Aitchison distance. 127, how were archaeal reads handled in both the case and cross-cohort studies? Besides bacteria, 16S sequencing can pick up archaea as well. Of interest, methanogenic archaea have been associated with IBS-C (doi.org/10.1007/s10620-021-06839-0). The Pozuelo cohort even had a specific focus on methanogenic archaea. It will be very interesting if the authors can pull all the archaeal reads from the previous cohorts for a mega-cohort analysis.
 We did not actively filter out the Archaeal reads. Retrospectively, we found that total 19 OTUs from the analyzed datasets were classified as Archaea. Of these, 12 belonged to Methanobrevibacter and Methanosphaera; the others were unclassifible below the phylum level. The prevalence of these known human methanogens genera varied greatly among the datasets.  Figure 1B). Our case-control study was limited in sample size, and other pediatric cohorts provided no subtype information.

Study
 Based on the adult mega-cohort, we performed a subtype comparison between HC vs IBS with and without diarrhea ( Figure 8E). We found that there was a lower α-diversity in patients with IBS with and without diarrhea compared to that of healthy controls. We added them following your recommendation.
166, The ASVs reproduced the same trend as the OTU analysis -that should be the main message here. Please try not to be overly obsessed with the 0.05 p cutoff, the 0.056 p for the D-vs HC is significant enough when considering both ASVs and OTUs.
 Thank you for the comment. We modified this part of the manuscript to emphasize the consistency among the OTU-and ASV-based results.
169-170, partial 16S can't reliably get to the species level -it'd be better to present both OTUs and ASVs instead in figure 4.
 We have addressed this issue as per the reviewer's previous comment.    represent and thus we modified the legend to strengthen and clarify our point.

Introduction
Line 41: We found that gut bacterias (typo)

 Corrected
Line 58: If IBS is likely to develop/or result in less diversity of gut microbes, how does antibiotic administration improve IBS?
 Small intestinal bacterial overgrowth (SIBO) may coexist with irritable bowel syndrome (IBS) in nearly half of the patients. Thus, eradication therapy has been reported as effective in reducing IBS symptoms.
The introduction is very light with minimal information---I only got one point which is IBS etiology is unknown and related somehow to microbiota structure.
The introduction needs to be more comprehensive, highlights the advances in the field, emphasize the gap, shows novelty of the study design or approach, or expected breakthrough.
 We appreciate your comment. We have now added the novelty of our study as per your suggestion.

Methods
Line 68: diagnosed with IBS (would the author describes how the diagnosis is made just) 3. After appropriate evaluation, the symptoms could not be fully explained by another medical condition.
The diagnosis of IBS was made with clinical histories using ROME IV. The final diagnosis implies no history of GIT disorders, such as inflammatory bowel disease, allergic GI diseases, or intestinal failure. Therefore, we removed 'No history of GIT disorder' to reduce confusion.
The inclusion criterion was children aged 4 -18 years diagnosed by the ROME IV criteria for children/adolescents.
Line 74: two weeks without antibiotics is not enough to restore normal gut flora (6 months at least)  Thank you for your comment. We recruited children who were not administered any antibiotics for at least two weeks prior to enrollment (visit 1). We collected their stools 2 ~ 4 weeks after the first visit (visit 2). Therefore, the wash-out period without antibiotics was 4~6 weeks. Other studies had one month wash-out period without antibiotics (Tap Some studies reported that the recovery periods depend on the types of antibiotics. For e.g., ampicillin (commonly used antibiotics in children) needs one month, vs. vancomycin and meropenem (not used in our patients for six months). We modified the sentences correctly and added relevant limitations.
Line 75: why being obese specifically is an exclusion criterion?
 Previous studies have shown that gut microbiota in obese children differs from healthy controls. Therefore, we excluded obese children to reduce bias. We also collected their medical histories; no children had chronic diseases or obesity histories.
Line 77: what do the authors mean by "abnormal endoscopic findings" and why this is an exclusion criterion?
 The inclusion criterion of IBS implies that no other accompanying GIT disorders exist.

Results
The conclusion is not supported by the results-In what way does this study introduce a rationale for new therapeutic trials?
 We agree with your comment and have corrected the conclusion as follows: To our knowledge, we performed the first cross-cohort analysis to find the association between IBS and gut microbial diversity and composition. It revealed that gut bacterial dysbiosis is associated with IBS, but the causal relationship is uncertain. Further studies are needed to ascertain whether the change in intestinal microorganisms contributes to developing IBS.

Suggestion:
A validation study is required For example: 1) Test the proinflammatory effect of Corynebacteriaceae and Clostridium clostridioforme on GIT cell lines such as Caco-2 2) Extract the microbiome cocktail from some stool samples (patients and control) and test the pro or anti-inflammatory effect on cell line---This will help to identify if the gut microbes play a role (as initiation or worsen of the IBS symptoms)  We appreciate your valuable advice. Future research should include validation studies as you comment rightly suggests.
December 10, 2022 1st Revision -Editorial Decision December 10, 2022 Prof. Jung Ok Shim Korea University College of Medicine 148, Gurodong-ro, Guro-gu Seoul 08308 Korea (South), Republic of Re: Spectrum02125-22R1 (Gut bacterial dysbiosis in irritable bowel syndrome; a case-control study and a cross-cohort analysis of publicly available datasets) Dear Prof. Jung Ok Shim: It's my pleasure to informa you that I have decided to accept your manuscript for publication in Microbiology Spectrum. Thank you for the efforts in addressing reviewer and editor concerns in your revised manuscript. You and your team have done an excellent job responding to all of our concerns, and the manuscript is substantially better as a result. I'm also particularly appreciative of the added sharing of data, and the expanded details provided in the materials and methods. Overall, I now feel it reads at the "level" of the potential impact of this work. Thus, I have also suggested to ASM's press office that this manuscript might be considered for highlighting in the press once published -but please understand I can't guarantee this.
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